If you are mailing something, using a plain formatted address string is usually fine.
Here's a harder problem from a previous job:
Given two addresses, tell me if they both point to the same "place".
(As you might imagine, the goal of this was joining various datasets.)
There really is no good way to do this, but to come close, you might consider a few dimensions of the problem:
1. Formatted address - a plain string. What you would write on an envelope.
2. Tokens (what I call "address components") - the bits of information that make up an address. Something like, {"street_number": "10", "street": "downing st", "locality": "london", ...}. You can get these back from most location-focused APIs.
3. Locations. Coordinates of a point or area in space.
4. Time. "Places" change over time. For example, a new business can open in place of another. Buildings are built and demolished. If you are working with data over a long time period, this is more of a concern.
Unfortunately, there are many-to-many relationships between all of these. For any address, there are many possible locations. For any location, there are many possible addresses. For any set of address components, there are many possible formatted addresses. For any address, there may have been multiple different "places" found there over time. Et cetera.
Here's a harder problem from a previous job:
Given two addresses, tell me if they both point to the same "place".
(As you might imagine, the goal of this was joining various datasets.)
There really is no good way to do this, but to come close, you might consider a few dimensions of the problem:
1. Formatted address - a plain string. What you would write on an envelope.
2. Tokens (what I call "address components") - the bits of information that make up an address. Something like, {"street_number": "10", "street": "downing st", "locality": "london", ...}. You can get these back from most location-focused APIs.
3. Locations. Coordinates of a point or area in space.
4. Time. "Places" change over time. For example, a new business can open in place of another. Buildings are built and demolished. If you are working with data over a long time period, this is more of a concern.
Unfortunately, there are many-to-many relationships between all of these. For any address, there are many possible locations. For any location, there are many possible addresses. For any set of address components, there are many possible formatted addresses. For any address, there may have been multiple different "places" found there over time. Et cetera.
Consider an office building, for an example. That building probably has at least one address corresponding to its main entrance, but it might have other entrances. An office within the building can sometimes use the building's main address as its own address, or the address of its own entrance, or an address with a floor number, or an address with a mailroom number. An office building may have a café (usually off of the lobby) that has its own addresses. Offices in the building can be split and merged over time. I have even seen multiple buildings merged into one. You might also have a co-working space with individual addresses for rooms within it.